Text Classification and Sentiment Analysis with Python Training Course

Course Introduction

Text Classification and Sentiment Analysis with Python Training Course is designed to provide participants with practical and comprehensive skills for processing, analysing, classifying, and interpreting textual data using Python. Organizations generate enormous quantities of unstructured text through customer reviews, emails, social media, surveys, support tickets, documents, online discussions, and other digital channels. The course introduces participants to the techniques required to transform this raw text into structured information and actionable analytical insights.

The programme provides a strong foundation in Natural Language Processing (NLP), covering the complete workflow from collecting and preparing textual datasets to building, evaluating, and deploying text classification and sentiment analysis models. Participants will learn how computers process human language and how text can be transformed into numerical representations suitable for machine learning. Practical exercises will introduce tokenization, normalization, stop-word handling, stemming, lemmatization, feature extraction, vectorization, and other essential text preprocessing techniques.

A major focus of the training is text classification, where participants learn how to automatically assign documents, messages, reviews, or other text into predefined categories. The course examines traditional machine learning approaches including Naive Bayes, Logistic Regression, Support Vector Machines, decision trees, and ensemble techniques. Participants will learn how to prepare labelled datasets, select appropriate features, train classification models, evaluate their performance, and interpret results using metrics such as accuracy, precision, recall, F1-score, and confusion matrices.

The course also provides extensive coverage of sentiment analysis, enabling participants to determine whether text expresses positive, negative, neutral, or more complex emotional characteristics. Participants will explore lexicon-based approaches as well as supervised machine learning and modern NLP techniques. They will learn how sentiment analysis can be applied to customer reviews, social media conversations, surveys, complaints, product feedback, and service interactions, while also understanding challenges such as sarcasm, ambiguity, context, slang, multilingual text, class imbalance, and domain-specific language.

Advanced modules introduce participants to deep learning and transformer-based NLP models. The training examines word embeddings, recurrent neural networks, Long Short-Term Memory networks, attention mechanisms, transformer architectures, and modern pretrained language models. Participants will learn how contemporary NLP systems can improve text classification and sentiment analysis by capturing contextual relationships between words and sentences. Practical attention is also given to fine-tuning, model evaluation, computational requirements, and responsible use of pretrained models.

By the end of the course, participants will be able to develop complete Python-based NLP workflows for text classification and sentiment analysis, from raw data preparation through model development, evaluation, visualization, and deployment. They will also understand how to select appropriate algorithms, diagnose model weaknesses, communicate analytical findings, and apply NLP responsibly. The training provides a strong foundation for applications in customer intelligence, social media analytics, market research, fraud detection, document processing, support automation, and other data-driven environments.

Duration

10 days

Who Should Attend

• Data Analysts and Business Intelligence Professionals

• Data Scientists and Machine Learning Practitioners

• Python Developers and Software Developers

• Artificial Intelligence and Machine Learning Professionals

• NLP Researchers and Practitioners

• Customer Experience and Customer Intelligence Professionals

• Marketing Analytics and Social Media Analytics Professionals

• Business Analysts Working With Text-Based Data

• Research and Survey Data Analysts

• Software Engineering Students and Graduates

• Computer Science and Information Technology Professionals

• Digital Transformation and Innovation Teams

• Customer Service and Contact Centre Analytics Professionals

• Academic Researchers Working With Unstructured Data

• Professionals Transitioning Into Data Science and AI

• Product and User Experience Analytics Professionals

• Developers Building AI-Powered Applications

• Professionals Seeking Practical Python NLP Skills

Course Objectives

• Understand the fundamental concepts of Natural Language Processing and how Python can be used to analyse and classify large volumes of textual information.

• Build complete text preprocessing workflows involving cleaning, normalization, tokenization, stop-word removal, stemming, lemmatization, and other preparation techniques.

• Transform unstructured text into numerical representations using methods such as Bag-of-Words, TF-IDF, n-grams, and modern embedding approaches.

• Develop supervised machine learning models for classifying text into predefined categories using labelled datasets and appropriate classification algorithms.

• Apply multiple classification techniques including Naive Bayes, Logistic Regression, Support Vector Machines, decision trees, and ensemble learning methods.

• Perform sentiment analysis on reviews, comments, survey responses, social media posts, and other textual data using multiple analytical approaches.

• Evaluate NLP models using accuracy, precision, recall, F1-score, confusion matrices, ROC-AUC, and other appropriate performance measures.

• Identify and address common NLP challenges including imbalanced datasets, noisy text, sarcasm, ambiguity, slang, spelling variations, and domain-specific terminology.

• Apply Python libraries and NLP frameworks to build reproducible workflows for text processing, feature engineering, machine learning, visualization, and model evaluation.

• Explore deep learning and transformer-based approaches for improving contextual understanding and performance in modern text classification applications.

• Fine-tune and evaluate appropriate pretrained NLP models for domain-specific classification and sentiment analysis tasks while considering computational requirements.

• Develop responsible NLP solutions that address data quality, privacy, bias, interpretability, security, ethical considerations, and appropriate human oversight.

Comprehensive Course Outline

Module 1: Introduction To Text Analytics And NLP

• Understanding Natural Language Processing, text analytics, text mining, and their practical applications

• Exploring structured versus unstructured data and the growing importance of textual information

• Examining real-world applications of text classification and sentiment analysis across industries

• Understanding the end-to-end NLP workflow from raw text collection to analytical insight and deployment

Module 2: Python Environment For NLP

• Setting up Python environments and installing essential libraries for text analytics and machine learning

• Working with Jupyter Notebook, Python scripts, virtual environments, and reproducible NLP projects

• Introducing NumPy, pandas, Matplotlib, and other Python tools commonly used in NLP workflows

• Importing, inspecting, organizing, and managing textual datasets using Python

Module 3: Text Data Collection And Exploration

• Collecting textual data from datasets, surveys, reviews, APIs, databases, and other appropriate sources

• Exploring text datasets to understand document length, vocabulary, missing values, duplicates, and data distributions

• Performing exploratory text analysis using word frequencies, document statistics, and basic visualizations

• Identifying data quality issues that can affect downstream classification and sentiment analysis models

Module 4: Text Preprocessing And Cleaning

• Cleaning textual data by removing unwanted characters, HTML elements, punctuation, and irrelevant content

• Applying tokenization, case normalization, stop-word removal, stemming, and lemmatization techniques

• Handling spelling variations, contractions, emojis, hashtags, URLs, usernames, numbers, and noisy social media text

• Designing preprocessing pipelines that preserve useful linguistic information while reducing unnecessary complexity

Module 5: Feature Engineering And Text Representation

• Converting textual documents into numerical features suitable for machine learning algorithms

• Applying Bag-of-Words, n-grams, frequency-based representations, and TF-IDF vectorization

• Understanding vocabulary construction, sparse matrices, feature selection, and dimensionality considerations

• Comparing traditional feature representations with semantic word and sentence embedding approaches

Module 6: Fundamentals Of Text Classification

• Understanding supervised text classification, labelled datasets, classes, features, training, and prediction

• Designing binary, multiclass, and multilabel classification problems for different text analytics applications

• Preparing training, validation, and test datasets while preventing data leakage and inappropriate evaluation

• Selecting classification approaches according to dataset size, class structure, interpretability, and application requirements

Module 7: Machine Learning Algorithms For Text Classification

• Building text classifiers using Naive Bayes and understanding its strengths for high-dimensional textual datasets

• Applying Logistic Regression and Support Vector Machines to document and message classification problems

• Exploring decision trees, random forests, gradient boosting, and ensemble approaches for text classification

• Comparing algorithms using consistent datasets, evaluation metrics, computational requirements, and practical objectives

Module 8: Sentiment Analysis Fundamentals

• Understanding sentiment analysis, opinion mining, polarity, subjectivity, and emotion-related text analytics

• Applying lexicon-based methods to identify positive, negative, and neutral sentiment in textual data

• Developing supervised sentiment classifiers using labelled reviews, comments, survey responses, and social media datasets

• Investigating practical challenges involving sarcasm, negation, context, mixed sentiment, slang, and ambiguous language

Module 9: Model Evaluation And Error Analysis

• Evaluating classification performance using accuracy, precision, recall, F1-score, and confusion matrices

• Understanding class imbalance and selecting appropriate metrics for different text classification applications

• Performing cross-validation, hyperparameter evaluation, threshold analysis, and model comparison

• Conducting error analysis to identify recurring model weaknesses and opportunities for improvement

Module 10: Advanced NLP And Word Embeddings

• Understanding distributed representations and how word embeddings capture semantic relationships between words

• Exploring Word2Vec, GloVe, FastText, and related embedding approaches for NLP applications

• Comparing static word embeddings with contextual representations generated by modern language models

• Applying embeddings to classification, similarity analysis, clustering, recommendation, and other text analytics tasks

Module 11: Deep Learning For Text Classification

• Introducing neural network architectures for text classification and sentiment analysis applications

• Exploring recurrent neural networks, Long Short-Term Memory networks, and sequence-based modelling

• Understanding attention mechanisms and their importance in improving contextual text representation

• Training and evaluating deep learning models while considering data requirements, overfitting, and computational resources

Module 12: Transformers And Modern NLP Models

• Understanding transformer architectures, self-attention, contextual representations, and modern NLP development

• Exploring pretrained language models such as BERT and related transformer-based architectures

• Applying transfer learning and fine-tuning techniques to domain-specific text classification problems

• Evaluating model performance, computational requirements, interpretability, and deployment considerations for transformer models

Module 13: Advanced Sentiment And Emotion Intelligence

• Moving beyond basic polarity classification toward emotion, intent, aspect, and topic-level sentiment analysis

• Applying aspect-based sentiment analysis to identify opinions about specific products, features, services, or attributes

• Combining sentiment information with topics, customer segments, channels, and time periods for deeper insight

• Analysing changing sentiment patterns to identify emerging issues, customer concerns, and experience opportunities

Module 14: NLP Visualization And Analytical Reporting

• Creating visualizations that communicate word frequencies, sentiment distributions, classification results, and model performance

• Developing analytical dashboards and reports for communicating NLP findings to technical and non-technical audiences

• Using Python visualization tools to explore relationships between text categories, sentiment, topics, and customer attributes

• Applying data storytelling principles to transform NLP outputs into clear and actionable analytical recommendations

Module 15: NLP Deployment, Automation And Responsible AI

• Building reusable Python pipelines for automated text classification and sentiment analysis workflows

• Exploring APIs, batch processing, model serving, and integration of NLP models into software applications

• Addressing privacy, security, bias, explainability, data protection, and responsible use of textual information

• Monitoring deployed NLP systems for model drift, changing language patterns, data quality problems, and performance degradation

Module 16: Emerging Trends In Text Analytics And Sentiment AI

• Exploring large language models, generative AI, retrieval-augmented generation, and their relationship with modern NLP

• Examining zero-shot, few-shot, instruction-based, and prompt-driven approaches to text classification

• Investigating multilingual NLP, multimodal language models, real-time sentiment intelligence, and domain-specific AI systems

• Developing future-ready strategies for combining traditional machine learning, transformers, generative AI, and human expertise

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.